The real network dataset MAHINDAS [1]} used for the case study is in the Economic Networks category. In this graph, vertices represent accounts, and edges represent transactions between them. The vertex size denotes the shortest cycle counting. The bigger a vertex, the more the shortest cycles pass through it. The vertex color denotes the shortest cycle length. The darker a vertex, the longer the shortest cycles. Figure REF  shows a subgraph centering at vertex 169, which all the shortest cycles through vertex 169 are listed. The figure matches the subgraph model in Figure REF . Vertices 281, 241, 169, 1159, and 888 are filtered out to be suspicious accounts for anomaly detection, e.g., money laundering. Based on these candidates derived from our algorithm, we could further analyse whether there is an exact case for the financial crime by enumerating such cycles or paths [2]}, [3]}
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